Radar SLAM for Autonomous Indoor Grinding

Proceedings of the IEEE National Radar Conference, pp. 1–6

Abstract

In the EU project Bots2Rec an autonomous asbestos reconstruction unit is developed by the Institute of Mechanism Theory, Machine Dynamics and Robotics (IGMR) of RWTH Aachen University. In the hazardous environment the mobile robot is expected to localize itself relative to the walls. Due to the heavy dust formation during and after grinding tasks, solely laser-based sensing is not reliable. Therefore, Bot2ReC deploys an additional 2D radar sensor to compensate for restricted visibility. In this work the developed online radar SLAM for indoor environments is presented. This method is based on the probabilistic iterative correspondence algorithm utilizing the Mahalanobis metric and improves it towards online capability. Further, a radar filter is deployed to reduce the data load.

Authors 4

1. Nils Mandischer Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   Institute of Mechanism Theory, Machine Dynamics and Robotics, Aachen, Germany

2. Sami Charaf Eddine Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   Institute of Mechanism Theory, Machine Dynamics and Robotics, Aachen, Germany

3. Mathias Huesing Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   Institute of Mechanism Theory, Machine Dynamics and Robotics, Aachen, Germany

4. Burkhard J. Corves Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   Institute of Mechanism Theory, Machine Dynamics and Robotics, Aachen, Germany